CN112733083B - Data verification method, system and device - Google Patents
Data verification method, system and device Download PDFInfo
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- CN112733083B CN112733083B CN201911032247.0A CN201911032247A CN112733083B CN 112733083 B CN112733083 B CN 112733083B CN 201911032247 A CN201911032247 A CN 201911032247A CN 112733083 B CN112733083 B CN 112733083B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0866—Checking the configuration
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
Abstract
The invention discloses a data verification method, a data verification system and a data verification device, and relates to the technical field of communication. Compared with the prior art, in the data verification method of the invention, the data to be verified comprises: and verifying the data to be verified according to the parameters contained in the data to be verified to obtain a verification result by at least one parameter of the traffic surface flow, the signaling surface index, the signaling surface record number, the actual HTTP packet number, the field filling rate, the logic accuracy or the core network element IP information. The invention can compare and check the parameters to be checked from at least one dimension, wherein when one aspect checks the abnormality, the data transaction structure is considered to be abnormal, thereby realizing the purpose of accurately checking the data.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method, system, and apparatus for verifying signaling XDR data.
Background
Analysis of mobile traffic is mainly performed in dependence on XDR data, the quality of which directly influences the quality of the data analysis.
Currently, the index value is obtained by mainly summarizing the XDR data, and then the index value is compared with the network management index to judge whether the XDR data is accurate or not. However, the network management index is an index of the network element level, and the XDR data is a service record of the user level, so that the XDR data can be compared with the index of the network element level after being summarized according to the network element, and whether the XDR data is accurate or not obviously cannot be reliably verified by comparing the index after the summarized.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method, a system and a device for data verification.
In a first aspect, the present invention provides a data verification method, where data to be verified is obtained, and the data to be verified includes: at least one parameter of service surface flow, signaling surface index, signaling surface record number, actual HTTP packet number, field filling rate, logic accuracy or core network element IP information;
and verifying the data to be verified according to the parameters contained in the data to be verified to obtain a verification result.
According to the data verification method, the data to be verified is verified according to the parameters contained in the data to be verified, so as to obtain a verification result, and the data verification method comprises the following steps:
when the data to be checked contains the service face flow, comparing the service face flow contained in the data to be checked with the service face flow in a network management platform, and when the difference between the service face flow and the service face flow is greater than a first threshold value, determining that the checking result is abnormal;
when the data to be checked comprises signaling surface indexes, comparing the signaling surface indexes included in the data to be checked with signaling surface indexes in a network management platform, and when the difference between the signaling surface indexes is greater than a first threshold value, determining that the checking result is abnormal;
When the data to be checked comprises the signaling plane record number, comparing the signaling plane record number included in the data to be checked with a signaling plane record number measurement value of sample data, and when the difference of the ratio of the signaling plane record number to the sample data is larger than a second threshold value, determining that the check result is abnormal;
when the data to be checked comprises service face flow, comparing the service face flow included in the data to be checked with a service face flow measurement value of sample data, and when the ratio difference of the service face flow and the service face flow is greater than a second threshold value, determining that the check result is abnormal;
when the data to be verified comprises the service plane record number, comparing the service plane record number included in the data to be verified with a service plane record number measurement value of sample data, and when the difference of the ratio of the service plane record number to the sample data is larger than a second threshold value, determining that the verification result is abnormal;
when the actual HTTP packet number is included in the data to be verified, comparing the actual HTTP packet number included in the data to be verified with the HTTP packet number calculated based on the corresponding relation between the HTTP packet number and the HTTP traffic number, and when the difference between the actual HTTP packet number and the HTTP traffic number is larger than a third threshold value, judging that the record logic of the data to be verified is abnormal; the ratio of the logic abnormal record number to the total HTTP record number is higher than a fourth threshold value, or the ratio of the total flow of the logic abnormal record number to the total flow of the HTTP record number is higher than a fifth threshold value, and a verification result is determined to be the data to be verified abnormal;
When the field filling rate is included in the data to be verified, acquiring the field filling rate, and when the field filling rate does not reach a sixth threshold value, determining that the verification result is abnormal;
when the logic accuracy rate is included in the data to be checked, acquiring the logic accuracy rate, and when the logic accuracy rate does not reach a sixth threshold value, determining that a check result is abnormal;
when the core network element IP information is included in the data to be checked, comparing the core network element IP information included in the data to be checked with a core network element IP information sample, and when the core network element IP information does not completely cover the core network element IP information sample, determining that the checking result is abnormal in the data to be checked.
The data verification method further comprises the following steps after the data to be verified is verified:
and if the checking result of the data to be checked is normal, taking the data to be checked as new sample data, and updating a checking model.
The data verification method comprises the following steps before verifying the data to be verified:
determining a target time period according to the current system date;
Selecting the data of the target time period as data to be verified;
wherein the determining the target time period according to the current system date comprises:
when the current system date is the first day of a verification period, emptying a selected time period array, randomly selecting a numerical value from a time period array to be selected to be a target time period, and storing the randomly selected numerical value into the selected time period array, wherein the verification period comprises a plurality of dates, the numerical value in the time period array to be selected comprises a plurality of initial time periods divided by the dates, and the total number of the dates of the verification period is larger than the total number of the initial time periods in the dates;
and when the current system date is the non-first day of the verification period, if the selected time period array is empty or the numerical value in the selected time period array covers all the initial time periods, randomly selecting the numerical value from the to-be-selected time period array to be set as a target time period, otherwise, randomly selecting the numerical value from the difference set of the to-be-selected time period array and the selected time period array to be set as a target time period, and storing the numerical value selected from the difference set array into the selected time period array.
The data verification method comprises the following steps before verifying the data to be verified:
establishing a verification model based on the sample data;
the verifying the data to be verified comprises the following steps: and checking the signaling plane record number, the service plane flow, the service plane record number, the HTTP packet number and the core network element IP information of the data to be checked by using the checking model.
In the above data verification method, the establishing a verification model based on the sample data includes:
taking the average value of at least two pieces of sample data in the same period to correspondingly calculate a signaling surface record number measurement value, a service surface flow measurement value and a service surface record number measurement value serving as the sample data according to the signaling surface record number, the service surface flow and the service surface record number;
based on the linear relation between the number of packets of the HTTP data packet and the number of HTTP traffic, determining a linear regression equation of the number of packets of the HTTP and the number of HTTP traffic as a corresponding relation between the number of packets of the HTTP and the number of HTTP traffic;
and storing network resource information in a network management platform, wherein the network resource information comprises core network element IP information samples.
The data verification method further comprises, before the verification sample is established: selecting sample data;
Wherein the selecting sample data comprises:
receiving interface data as a sample to be selected;
when the difference of the ratio of the flow number of the time period corresponding to the sample to be selected to the flow number counted by the network management platform is smaller than an eighth threshold value, comparing the signaling surface index of the sample to be selected with the index of the network management platform, and judging whether the signaling surface index is within an allowable range or not;
if yes, comparing the core network element information of the sample to be selected with the network element information of the network management platform, and judging whether the core network element information and the network element information are consistent;
if yes, calculating whether the field filling rate and the logic accuracy of the sample to be selected reach a ninth threshold value;
if yes, the sample to be selected is selected as sample data.
In a second aspect, the present invention provides a data verification system, including a data collector and a server; the data acquisition device acquires data to be verified;
the server comprises a first port, a memory and a processor;
the first port is used for receiving data to be verified;
the processor calculates and checks the data to be checked according to the data to be checked, and outputs a check result;
and the memory stores the data to be checked and the check result.
In a third aspect, the present invention provides an apparatus comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor performs the steps of the method as claimed in any one of the preceding claims.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method as described in any of the preceding claims.
Compared with the prior art, in the data verification method of the invention, the data to be verified comprises: and verifying the data to be verified according to the parameters contained in the data to be verified to obtain a verification result by at least one parameter of the traffic surface flow, the signaling surface index, the signaling surface record number, the actual HTTP packet number, the field filling rate, the logic accuracy or the core network element IP information. The invention can compare and check the parameters to be checked from at least one dimension, wherein when one aspect checks the abnormality, the data transaction structure is considered to be abnormal, thereby realizing the purpose of accurately checking the data.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a data verification method according to an exemplary embodiment of the invention;
FIG. 2 is a flow chart of selecting sample data in FIG. 1;
FIG. 3 is an example of a scatter pattern made based on the number of packets and throughput of HTTP samples in sample data;
fig. 4 is a diagram showing checking network element names by using network element IP in XDR data to be checked as an index;
fig. 5 is a diagram of checking network element IP with the network element name in the XDR to be checked as an index;
FIG. 6 is a diagram of an index checking industry unit name with the APN name in the XDR to be checked;
FIG. 7 is a diagram showing checking of APN names with industry unit names in XDR to be checked as indexes;
FIG. 8 is a flowchart of S06 and S08 according to an embodiment of the present invention;
fig. 9 is a block diagram of a data verification system according to still another exemplary embodiment of the present invention.
Reference numerals:
200-a data verification system; 210-a data collector; 220-server.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments of the present invention and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the data verification method provided by the invention, the time period, the initial time period, the time period to be selected, the selected time period and the like all refer to a plurality of time periods in one day, and one hour can be taken as one time period, and half hour or two hours can be taken as one time period. If one hour is taken as a time period, then 24 time periods are included within a date, such as 13:00pm-14:00pm,14:00pm-15:00pm, and 15:00pm-16:00pm, etc. Fig. 1 is a flowchart of a data verification method according to an embodiment of the present invention.
S02: sample data is selected.
The quality of the sample data determines the accuracy of the data verification result, so that the sample data should meet the relevant requirements.
Fig. 2 is a flowchart of selecting sample data, which specifically includes the following steps.
S021: interface data is received as a sample to be selected. Specifically, the interface data is XDR data, so the sample to be selected is an XDR sample to be selected.
S022: and when the difference of the flow number corresponding to the sample to be selected and the flow number counted by the network management platform is smaller than an eighth threshold value, comparing the signaling surface index of the sample to be selected with the index of the network management platform, and judging whether the signaling surface index is within the allowable range. Specifically, comparing the traffic number of the service surface of the time period corresponding to the sample to be selected with the traffic number counted by the network management platform, and if the difference between the traffic number and the traffic number is within a preset N% (eighth threshold), counting the signaling surface index of the sample to be selected and comparing the signaling surface index with the index of the network management platform; otherwise, outputting a report that the problem exists in acquisition of DPI equipment or XDR synthesis, and entering a problem investigation and processing link. If the difference between the signaling surface index of the sample to be selected and the index of the network management platform is within the preset M% (tenth threshold), S023 is entered, otherwise, a report that the problem exists in acquisition of DPI equipment or XDR synthesis is output, and a problem investigation and processing link can be entered. The eighth threshold value and the tenth threshold value may take the values of 1%, 5%, and so on.
Thus, S022 may include S0221 and S0222.
S0221: and judging whether the difference between the traffic number of the service surface of the time period corresponding to the sample to be selected and the traffic number counted by the network management platform is larger than an eighth threshold value, and if not, entering S0222.
S0222: and judging whether the difference between the signaling surface index of the sample to be selected and the index of the network management platform is larger than a tenth threshold value, and if not, entering S023.
The indexes of the network management platform comprise service surface flow and signaling surface indexes.
S023: and comparing the core network element information of the sample data to be selected with the network element information of the network management platform, and judging whether the core network element information and the network element information are consistent. And comparing the core network element information of the sample to be selected with the network element information of the network management platform, if the core network element information is consistent with the network element information of the network management platform, entering S024, otherwise, identifying that the core network element information of the sample data is missing, and entering a problem investigation and processing link.
S024: and calculating whether the field filling rate and the logic accuracy rate of the sample data to be selected reach a ninth threshold value.
Where the field filling rate is the rate at which the field value is not null, it is generally required that the field filling rate reaches 99%. Thus, the ninth threshold may take on values of 99%, 98%, 97%, etc. Fields for statistical field fill rate include, but are not limited to, the following: MSISDN, IMSI, IMEI, PCI, CELL-ID, TAC, local City, PGW\GGSN IP Add, SGSN IP Add, SGW IP Add, APN, APP TYPE, APP SUB TYPE.
The logic accuracy is to check whether the field accords with logic, for example, when the check field is TAC, the value should be 0-65535, but when the check field is checked, the value is filled with "FFFFF", or the value is greater than 65535, the logic error is indicated. The fields logically checked in embodiments of the present invention include, but are not limited to, the fields in table 1 below.
TABLE 1
S025: and if the field filling rate and the logic accuracy rate of the sample data to be selected reach the ninth threshold value, selecting the sample to be selected as the sample data. Thus, the selection of sample data is completed.
The above-mentioned problem investigation and processing links may be to reselect samples, or to perform investigation on DPI equipment, etc.
S04: a verification model is established based on the sample data, and the following steps are included in S04.
S041: and taking the average value of at least two pieces of sample data in the same period to correspondingly calculate the signaling surface record number measurement value, the service surface flow measurement value and the service surface record number measurement value serving as the sample data according to the signaling surface record number, the service surface flow and the service surface record number. The signaling plane record number measurement value, the traffic plane traffic measurement value and the traffic plane record number measurement value are average values of corresponding parameters calculated from at least three sample data of the same time period. The measurement values of the respective parameters can be calculated from sample data of the same period of time of four, five, or the like. The signaling plane record number is specifically the signaling plane XDR record number, and the service plane record number is specifically the service plane XDR record number. In particular, during the verification process, the measurement values of the parameters may be used to compare with the aggregate values of the parameters of the data to be verified.
S042: based on the linear relation between the number of packets of the HTTP data packet and the number of HTTP traffic, determining a linear regression equation of the number of HTTP packets and the number of HTTP traffic as the corresponding relation between the number of HTTP packets and the number of HTTP traffic.
Because the more packets of the same type are sent or received, the more interactive traffic is generated, and the ratio of HTTP traffic in data service traffic is over 90%, the HTTP sample in the sample data is used for analysis, and a linear regression equation of HTTP packet number and HTTP traffic number is established.
Fig. 3 is an example of a scatter pattern based on the number of packets and throughput of HTTP samples in sample data, and a scatter diagram created based on sample data in the actual data verification process is not limited to fig. 3, and may be different from fig. 3. In fig. 3, the abscissa indicates throughput and the ordinate indicates the number of packets. From fig. 3, it is clear that the linear relationship between HTTP packets and HTTP traffic is more obvious, the square of R indicates how much is the change of independent variable, the square of R is the ratio of the sum of squares of regression to the sum of squares of total dispersion, and the better this ratio is, the more accurate the model is, and the more obvious the regression effect is. The closer R square is between 0 and 1 to 1, the better the regression fitting effect is, and the model fitting goodness exceeding 0.8 is considered to be higher, and the calculation formula is as follows:
R^2＝r*r
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the square of R was found to be 0.979 in FIG. 3, indicating that the regression fit was excellent.
The linear regression equation can be found by the least square method:
where x, y, n are the throughput, the number of packets, and how many sets of samples,and the average value of x and y is respectively calculated, and values of a and b are 13.021706 and 0.001064 respectively according to a substitution formula, so that a linear regression equation is as follows: y=13.021706+0.001064 x.
After determining the linear regression equation, the corresponding packet number can be calculated after knowing the flow number.
S043: and storing network resource information in a network management platform, wherein the network resource information comprises core network element IP information samples.
The core network element IP information includes a network element IP and a network element name.
According to the DPI specification, the IP information samples of the core network elements corresponding to the interfaces are shown in Table 2.
TABLE 2
In the subsequent verification process of the data to be verified, if the corresponding core network element IP information is not retrieved from the core network element IP information sample in the data to be verified, the data to be verified can be considered to have missing abnormality.
In the embodiment of the invention, the network resource information can comprise a network element name corresponding to the core network element IP sample, and the network element name is used for checking whether the network element name in the XDR data to be checked is backfilled accurately. The network element names and the network element IPs are respectively indexed, and when the corresponding relation of the core network element IP information (the core network element IP information comprises the network element names and the network element IPs) retrieved by the system in the XDR data to be checked in the resource information does not exist, the network resource information is required to be updated and matched.
And accurately judging and realizing logic reference diagrams 4 and 5 by the core network element IP information in the XDR data backfill to be checked. Fig. 4 is a diagram showing that the network element IP in the XDR data to be checked is used as an index to check the network element name, for example, the network element IP (a) has a corresponding network element name b, the network resource information stored in the network management platform finds the network element name b' through the network element IP (a), and if a plurality of different network element names exist, the backfill is abnormal; if not, judging whether b is null, if yes, then indicating that the XDR data bottom network resource information is not complete, and updating the network resource information is needed, otherwise, judging whether b is equal to b', if yes, backfilling correctly, otherwise, backfilling incorrectly, and updating is needed. Fig. 5 is a diagram showing that the network element name in the XDR to be checked is used as an index to check the network element IP, for example, the network element name e has a corresponding network element IP (f), the network resource information stored in the network management platform finds a corresponding network element IP (f ') through the network element name 3, and judges whether f is a null value, if yes, the network resource information at the XDR data bottom layer is not full, the network resource information needs to be updated, otherwise, judges whether f is equal to f', if yes, the backfill is correct, otherwise, the backfill is wrong, and the update needs to be performed.
The XDR data to be checked can carry out backfill check on APN industry units and APN names besides the backfill check between the network element IP and the network element names. And accurately judging and realizing logic reference diagrams 6 and 7 by APN industry unit information in the XDR data backfill to be checked.
Fig. 4, fig. 5, fig. 6 and fig. 7 show that the network resource information stored by the network management platform is comprehensive if all backfilling is correct.
S06: and determining a target time period according to the current system date. Specifically, S06 includes S061 and S062.
And S061, when the current system date is the first day of the verification period, emptying the selected time period array, randomly selecting a numerical value from the time period array to be selected to be a target time period, and storing the randomly selected numerical value into the selected time period array, wherein the verification period comprises a plurality of dates, the numerical value in the time period array to be selected comprises a plurality of initial time periods divided by the dates, and the total number of the dates of the verification period is larger than the total number of the initial time periods in the dates.
S062: and when the current system date is the non-first day of the verification period, if the selected time period is empty or the values in the selected time period array cover all the initial time periods, randomly selecting the values from the to-be-selected time period array to be set as a target time period, otherwise, selecting the values from the difference set of the to-be-selected time period array and the selected time period array to be set as the target time period, and storing the values selected in the difference set array into the selected time period array.
Wherein the verification period may be one month, or one worship. If the verification period is one month, 1 day of each month may be the first day of the verification period, or 16 days of each month may be the first day of the verification period. How the target time period is determined based on the current system date is described below with the first day of the 1-day-of-month check period and the initial time period with one time period per hour as an example.
The time period array to be selected stores 24 time period values in advance. When the current system date is 1 day, the values stored in the selected time period array are cleared, and the selected time period array is randomly selected from the to-be-selected time period array to be set as a target time period, for example, the selected time period 9 is: 00am-10:00am, then time period 9:00am-10:00am is stored in the array of selected time periods. When the current system date is not 1 day per month, if the selected time period array is empty (indicating that the system has just started) or the values in the selected time period array have covered all the initial time periods (i.e., 24 time periods are included in the selected time period values), the value setting target time period is randomly selected from the to-be-selected time period array. Otherwise, the difference set array is obtained by taking the difference value between the array of the time periods to be selected and the array of the time periods to be selected, for example, the time period 10:00am-11:00am and time period 11:00am-12: if none of 00am has an array of selected time periods, the difference set array includes time period 10:00am-11:00am and time period 11:00am-12:00am, thus can be measured from time frame 10:00am-11:00am and time period 11:00am-12: randomly selecting one time period value from the two 00am values to be set as a target time period, and if time period 11 is selected: 00am-12:00am, the value is stored in the array of selected time periods.
S08: and selecting the data of the target time period as the data to be verified. Specifically, in order to ensure that the target time period is closest to the system time, when the target time period lags behind the current system time and the lag value is larger than a seventh threshold value, selecting the data of the target time period of the current system date as the data to be checked; otherwise, selecting the data of the target time period of the previous system date as the data to be checked. For example, the number of the cells to be processed,
and (3) selecting XDR data of 3:00pm-4:00pm on the same day as data to be checked if the randomly selected target time period is 3:00pm-4:00pm, the current system time is 4:30pm, and the seventh threshold is set to 15 minutes. If the randomly selected target time period is 3:00pm-4:00pm, the current system time is 1:00pm, and the seventh threshold is set to 15 minutes, XDR data of 3:00pm-4:00pm on the previous day are selected as data to be checked. The flow of S06 and S08 may refer to fig. 8, where an array a in fig. 8 is an array of the time periods to be selected, an array B in fig. 8 is an array of the time periods to be selected, an array C in fig. 8 is an array of a difference between the array of the time periods to be selected and the array of the time periods to be selected, and N is a seventh threshold.
S010: and checking the data to be checked.
S010 includes steps S010-1 and S010-2.
S010-1: obtaining data to be verified, wherein the data to be verified comprises: at least one parameter of service surface flow, signaling surface index, signaling surface record number, actual HTTP packet number, field filling rate, logic accuracy or core network element IP information. The more the variety of parameters in the data to be checked, the higher the dimension capable of checking, so that the more comprehensive checking from multiple dimensions can be realized, and the erroneous checking result is avoided being output. Thus, the data to be verified may also include: the service plane flow, the signaling plane index, the number of signaling plane records, the number of actual HTTP packets, the field filling rate, the logic accuracy or two, three or four parameters in the core network element IP information, etc. are not described in detail.
S010-2: and verifying the data to be verified according to the parameters contained in the data to be verified to obtain a verification result. For example, judging whether the data to be checked is abnormal or not according to the parameter value of the service face flow in the data to be checked; or judging whether the data to be checked is abnormal or not according to the parameter values of the two parameters, namely the traffic surface flow and the signaling surface record number in the data to be checked; and so on, will not be described in detail.
Specifically, S010-2 includes the following verification process.
When the data to be checked contains service face flow, the service face flow contained in the data to be checked is compared with the service face flow in the network management platform, when the difference between the service face flow and the service face flow is larger than a first threshold value, the XDR data is abnormal, and the checking result is determined to be the data to be checked abnormal.
When the signaling surface index is included in the data to be checked, the signaling surface index included in the data to be checked is compared with the signaling surface index in the network management platform, when the difference between the signaling surface index and the signaling surface index is larger than a first threshold value, the XDR data is abnormal, and the checking result is determined to be the data to be checked abnormal.
When the signaling plane record number is included in the data to be checked, comparing the signaling plane record number included in the data to be checked with the signaling plane record number measurement value of the sample data, and when the ratio difference of the signaling plane record number and the signaling plane record number is larger than a second threshold value, deleting the XDR data, and determining that the checking result is abnormal.
When the service face flow is included in the data to be checked, the service face flow included in the data to be checked is compared with the service face flow measurement value of the sample data, when the ratio difference of the service face flow and the service face flow is larger than a second threshold value, the XDR data is deleted, and the checking result is determined to be the data to be checked is abnormal.
When the service plane record number is included in the data to be checked, comparing the service plane record number included in the data to be checked with the service plane record number measurement value of the sample data, and when the difference of the ratio of the service plane record number to the sample data is larger than a second threshold value, deleting the XDR data, and determining that the checking result is abnormal.
When the actual HTTP packet number is included in the data to be checked, comparing the actual HTTP packet number included in the data to be checked with the HTTP packet number calculated based on the corresponding relation between the HTTP packet number and the HTTP traffic number, and when the difference between the actual HTTP packet number and the HTTP traffic number is larger than a third threshold value, judging that the record logic of the data to be checked is abnormal; and if the ratio of the logic abnormal record number to the total HTTP record number is higher than a fourth threshold value or the ratio of the total flow of the logic abnormal record number to the total flow of the HTTP record number is higher than a fifth threshold value, judging that the XDR data is logically abnormal, and determining that the verification result is the data to be verified is abnormal.
When the field filling rate is included in the data to be checked, the field filling rate is obtained, when the field filling rate does not reach a sixth threshold value, the XDR data is not standard and the data are abnormal, and the checking result is determined to be the data to be checked abnormal.
When the logic accuracy rate is included in the data to be checked, the logic accuracy rate is obtained, when the logic accuracy rate does not reach a sixth threshold value, the XDR data is not standard and the data are abnormal, and the checking result is determined to be the data to be checked abnormal.
When the core network element IP information is included in the data to be checked, comparing the core network element IP information included in the data to be checked with the core network element IP information sample, and when the core network element IP information does not completely cover the core network element IP information sample, determining that the checking result is abnormal in the data to be checked. Wherein the core network element IP information comprises a network element IP and a network element name
The difference between the traffic surface flow of the data to be checked and the signaling surface index and the traffic surface flow of the network management platform and the signaling surface index is larger than a first threshold, which means that the difference between the traffic surface flow of the data to be checked and the traffic surface flow of the network management platform is larger than the first threshold, or the difference between the signaling surface index of the data to be checked and the signaling surface index of the network management platform is larger than the first threshold, or both are larger than the first threshold. In practice, in the data verification process, the traffic surface flow and the signaling surface index of the data to be verified may not be compared with the traffic surface flow and the signaling surface index of the network management platform, for example, only the difference between the signaling surface index of the data to be verified and the signaling surface index of the network management platform is compared, but the traffic surface flow of the data to be verified is not compared with the traffic surface flow of the network management platform.
Specifically, in step S010, the verification includes verifying the number of signaling plane records, traffic plane traffic, the number of traffic plane records, the number of HTTP packets, and the core network element IP information of the data to be verified by using the verification model.
The field filling rate and the logic accuracy rate of the XDR to be checked need to reach 99%, if not, the XDR data is not standard and the data is abnormal, so that the sixth threshold value can be 99%, or 98%, 97% or the like. The first threshold value, the second threshold value, the third threshold value, the fourth threshold value, the fifth threshold value, and the like may be the same as the sixth threshold value or may not be the same.
Between steps S08 and S010, the method may further include calculating data to be verified for subsequent data verification, where the calculating content includes: traffic plane traffic of the XDR data to be checked, the number of traffic plane XDR records, the signaling plane of the XDR data to be checked, the number of XDR records, signaling plane indexes (indexes which can be counted by a network management platform and can be calculated by XDR, such as an attach success rate, a default bearer establishment success rate, a TAU update success rate, etc., are selected according to practical situations), field filling rate and field logic accuracy of the XDR data to be checked are calculated, and the HTTP packet number is predicted according to a linear regression equation y=13.021706+0.001064 x.
In addition, the core network element IP information after the data to be checked are summarized and de-duplicated can be compared with the core network element IP information sample. Because the core network element IP information after summarizing and de-duplicating the data to be checked is required to be compared with the core network element IP information sample, de-duplicating a large number of XDR records consumes huge system resources and calculation time. For this purpose, the embodiment of the invention adopts a random sampling algorithm to carry out the check after the de-duplication, and comprises the following steps:
s0101: reading the set value of N (sampling times, initial for example, 5) and T (interval between sampling start time and sampling end time, for example, 20 minutes);
s0102: judging the value of N, if N is 0, indicating that N times are extracted and the core network IP information in the XDR data to be checked is incomplete, judging that the XDR data to be checked is incomplete, ending the checking flow of the core network element IP information, otherwise entering S0103;
s0103: randomly generating a starting time in the verification period, wherein the time +T is required to not exceed the verification period (if the data of 13:00-14:00 are verified, S+T cannot exceed 14 points);
s0104: it is determined whether the start time of generation has been previously generated, preventing sampling of data for the same period of time. Returning to S0103 if the time is generated, and storing the time into the array L and entering S0105 if the time is not generated;
S0105: summarizing the IP information of the core network element in the XDR data to be checked in the period from S to S+T;
s0106: comparing the calculation result of the S0105 with the core network element IP information sample, if the core network element IP information is complete, judging that the core network element IP information of the data to be checked is normal, and ending the checking flow. Otherwise, the N value is reduced by 1 and then the process goes to S0102.
S012, after the data to be checked is checked, judging the checking result, if the checking result of the data to be checked is normal, turning to step S014, otherwise turning to step S016.
S014: and taking the data to be checked as new sample data, and updating the check model.
Because the flow and the total XDR record number can change along with the system upgrade, the sample data of the XDR should be updated along with the system upgrade, the old sample data should be discarded to compensate the new sample data, after the data verification is completed, if the data to be verified is judged to be normal, the XDR data is used as the new sample data, and the extracted measurement value (XDR flow, record number and the like) is stored in the verification model to be used as the basis for the next result judgment.
In addition, S014 further includes: and outputting verification period and verification items, wherein the verification period and verification items comprise verification with network management indexes, verification with XDR samples, verification with linear regression models, field normalization verification (filling rate and logic accuracy), network resource verification, calculation results, differences, set thresholds and the like of various verification items.
S016: an alert is generated. S106 may further include displaying items of verification period and verification, including verification with network management indexes, verification with XDR samples, verification with linear regression models, verification with field normalization (filling rate, logic accuracy), verification of network resources, calculation results, differences, set thresholds, and other contents of various verification items, and highlighting the verification item for the occurrence of abnormalities.
Fig. 9 is a block diagram of a data verification system according to still another exemplary embodiment of the present application. The data verification system 200 includes a data collector 210 and a server 220.
The data collector 210 collects data to be verified. The server 220 includes a first port, a memory, and a processor, where the first port is used for checking data, and the processor calculates and checks the data according to the data to be checked, and outputs a checking result. And the memory stores the data to be checked and the check result.
The service management system provided by the embodiment of the present application may also execute the method executed by the data verification system in fig. 1, and implement the functions of the data verification system in the embodiment shown in fig. 1, which are not described herein again.
The embodiment of the application also provides a device, which comprises: the data verification method comprises the steps of a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the computer program is executed by the processor to realize the data verification method.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the respective processes of the above-mentioned data verification method embodiment, and can achieve the same technical effects, and in order to avoid repetition, the description is omitted here. Wherein the computer readable storage medium is selected from Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
While the foregoing is directed to embodiments of the present invention, other and further details of the invention may be had by the present invention, it should be understood that the foregoing description is merely illustrative of the present invention and that no limitations are intended to the scope of the invention, except insofar as modifications, equivalents, improvements or modifications may be made within the spirit and principles of the invention.
Claims (8)
1. A data verification method is characterized in that,
obtaining data to be verified, wherein the data to be verified comprises: at least one parameter of service surface flow, signaling surface index, signaling surface record number, actual HTTP packet number, field filling rate, logic accuracy or core network element IP information;
according to parameters contained in the data to be verified, verifying the data to be verified to obtain a verification result;
and verifying the data to be verified according to parameters contained in the data to be verified to obtain a verification result, wherein the verification result comprises the following steps:
when the data to be checked contains the service face flow, comparing the service face flow contained in the data to be checked with the service face flow in a network management platform, and when the difference between the service face flow and the service face flow is greater than a first threshold value, determining that the checking result is abnormal;
When the data to be checked comprises signaling surface indexes, comparing the signaling surface indexes included in the data to be checked with signaling surface indexes in a network management platform, and when the difference between the signaling surface indexes is greater than a first threshold value, determining that the checking result is abnormal;
when the data to be checked comprises the signaling plane record number, comparing the signaling plane record number included in the data to be checked with a signaling plane record number measurement value of sample data, and when the difference of the ratio of the signaling plane record number to the sample data is larger than a second threshold value, determining that the check result is abnormal;
when the data to be checked comprises service face flow, comparing the service face flow included in the data to be checked with a service face flow measurement value of sample data, and when the ratio difference of the service face flow and the service face flow is greater than a second threshold value, determining that the check result is abnormal;
when the data to be verified comprises the service plane record number, comparing the service plane record number included in the data to be verified with a service plane record number measurement value of sample data, and when the difference of the ratio of the service plane record number to the sample data is larger than a second threshold value, determining that the verification result is abnormal;
When the actual HTTP packet number is included in the data to be verified, comparing the actual HTTP packet number included in the data to be verified with the HTTP packet number calculated based on the corresponding relation between the HTTP packet number and the HTTP traffic number, and when the difference between the actual HTTP packet number and the HTTP traffic number is larger than a third threshold value, judging that the record logic of the data to be verified is abnormal; the ratio of the logic abnormal record number to the total HTTP record number is higher than a fourth threshold value, or the ratio of the total flow of the logic abnormal record number to the total flow of the HTTP record number is higher than a fifth threshold value, and a verification result is determined to be the data to be verified abnormal;
when the field filling rate is included in the data to be verified, acquiring the field filling rate, and when the field filling rate does not reach a sixth threshold value, determining that the verification result is abnormal;
when the logic accuracy rate is included in the data to be checked, acquiring the logic accuracy rate, and when the logic accuracy rate does not reach a sixth threshold value, determining that a check result is abnormal;
when the to-be-checked data comprises core network element IP information, comparing the core network element IP information included in the to-be-checked data with a core network element IP information sample, and when the core network element IP information does not completely cover the core network element IP information sample, determining that a checking result is abnormal in the to-be-checked data;
After the data to be verified is verified, the method further comprises:
and if the checking result of the data to be checked is normal, taking the data to be checked as new sample data, and updating a checking model.
2. The data verification method according to claim 1, comprising, before the verifying the data to be verified:
determining a target time period according to the current system date;
selecting the data of the target time period as data to be verified;
wherein the determining the target time period according to the current system date comprises:
when the current system date is the first day of a verification period, emptying a selected time period array, randomly selecting a numerical value from a time period array to be selected to be a target time period, and storing the randomly selected numerical value into the selected time period array, wherein the verification period comprises a plurality of dates, the numerical value in the time period array to be selected comprises a plurality of initial time periods divided by the dates, and the total number of the dates of the verification period is larger than the total number of the initial time periods in the dates;
and when the current system date is the non-first day of the verification period, if the selected time period array is empty or the numerical value in the selected time period array covers all the initial time periods, randomly selecting the numerical value from the to-be-selected time period array to be set as a target time period, otherwise, randomly selecting the numerical value from the difference set of the to-be-selected time period array and the selected time period array to be set as a target time period, and storing the numerical value selected from the difference set array into the selected time period array.
3. The data verification method according to claim 1, comprising, before the verifying the data to be verified:
establishing a verification model based on the sample data;
the verifying the data to be verified comprises the following steps: and checking the signaling plane record number, the service plane flow, the service plane record number, the HTTP packet number and the core network element IP information of the data to be checked by using the checking model.
4. A data verification method according to claim 3, wherein said establishing a verification model based on sample data comprises:
taking the average value of at least two pieces of sample data in the same period to correspondingly calculate a signaling surface record number measurement value, a service surface flow measurement value and a service surface record number measurement value serving as the sample data according to the signaling surface record number, the service surface flow and the service surface record number;
based on the linear relation between the number of packets of the HTTP data packet and the number of HTTP traffic, determining a linear regression equation of the number of packets of the HTTP and the number of HTTP traffic as a corresponding relation between the number of packets of the HTTP and the number of HTTP traffic;
and storing network resource information in a network management platform, wherein the network resource information comprises core network element IP information samples.
5. A data verification method according to claim 3, further comprising, prior to establishing the verification sample: selecting sample data;
Wherein the selecting sample data comprises:
receiving interface data as a sample to be selected;
when the difference of the ratio of the flow number of the time period corresponding to the sample to be selected to the flow number counted by the network management platform is smaller than an eighth threshold value, comparing the signaling surface index of the sample to be selected with the index of the network management platform, and judging whether the signaling surface index is within an allowable range or not;
if yes, comparing the core network element information of the sample to be selected with the network element information of the network management platform, and judging whether the core network element information and the network element information are consistent;
if yes, calculating whether the field filling rate and the logic accuracy of the sample to be selected reach a ninth threshold value;
if yes, the sample to be selected is selected as sample data.
6. The data verification system is characterized by comprising a data acquisition unit and a server; the data acquisition device acquires data to be verified; the data to be verified comprises: at least one parameter of service surface flow, signaling surface index, signaling surface record number, actual HTTP packet number, field filling rate, logic accuracy or core network element IP information;
the server comprises a first port, a memory and a processor;
the first port is used for receiving data to be verified;
The processor calculates and checks the data to be checked according to parameters contained in the data to be checked, and outputs a check result;
the memory stores the data to be checked and the checking result;
the processor calculates and checks the data to be checked according to parameters contained in the data to be checked, and outputs a check result, including:
when the data to be checked contains the service face flow, comparing the service face flow contained in the data to be checked with the service face flow in a network management platform, and when the difference between the service face flow and the service face flow is greater than a first threshold value, determining that the checking result is abnormal;
when the data to be checked comprises signaling surface indexes, comparing the signaling surface indexes included in the data to be checked with signaling surface indexes in a network management platform, and when the difference between the signaling surface indexes is greater than a first threshold value, determining that the checking result is abnormal;
when the data to be checked comprises the signaling plane record number, comparing the signaling plane record number included in the data to be checked with a signaling plane record number measurement value of sample data, and when the difference of the ratio of the signaling plane record number to the sample data is larger than a second threshold value, determining that the check result is abnormal;
When the data to be checked comprises service face flow, comparing the service face flow included in the data to be checked with a service face flow measurement value of sample data, and when the ratio difference of the service face flow and the service face flow is greater than a second threshold value, determining that the check result is abnormal;
when the data to be verified comprises the service plane record number, comparing the service plane record number included in the data to be verified with a service plane record number measurement value of sample data, and when the difference of the ratio of the service plane record number to the sample data is larger than a second threshold value, determining that the verification result is abnormal;
when the actual HTTP packet number is included in the data to be verified, comparing the actual HTTP packet number included in the data to be verified with the HTTP packet number calculated based on the corresponding relation between the HTTP packet number and the HTTP traffic number, and when the difference between the actual HTTP packet number and the HTTP traffic number is larger than a third threshold value, judging that the record logic of the data to be verified is abnormal; the ratio of the logic abnormal record number to the total HTTP record number is higher than a fourth threshold value, or the ratio of the total flow of the logic abnormal record number to the total flow of the HTTP record number is higher than a fifth threshold value, and a verification result is determined to be the data to be verified abnormal;
when the field filling rate is included in the data to be verified, acquiring the field filling rate, and when the field filling rate does not reach a sixth threshold value, determining that the verification result is abnormal;
When the logic accuracy rate is included in the data to be checked, acquiring the logic accuracy rate, and when the logic accuracy rate does not reach a sixth threshold value, determining that a check result is abnormal;
when the to-be-checked data comprises core network element IP information, comparing the core network element IP information included in the to-be-checked data with a core network element IP information sample, and when the core network element IP information does not completely cover the core network element IP information sample, determining that a checking result is abnormal in the to-be-checked data;
and the data verification system is also used for taking the data to be verified as new sample data and updating a verification model if the verification result of the data to be verified is normal after the data to be verified is verified.
7. An apparatus, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, performs the steps of the method according to any one of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 5.
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